| lmrob.control {robustbase} | R Documentation |
Tuning parameters for, lmrob, the MM-regression
estimator and the associated S-estimator.
lmrob.control(seed = 37, Nres = 500,
tuning.chi = 1.54764, bb = 0.5, tuning.psi = 4.685061,
max.it = 50, groups = 5, n.group = 400, k.fast.s = 1,
compute.rd = TRUE)
seed |
random seed for the re-samples used in obtaining candiates
for the initial S-estimator. The default, 37 used to be
frozen in the underlying C code. |
Nres |
number of re-sampling candidates to be used to find the initial S-estimator. Currently defaults to 500 which works well in most situations (see References below). User-choice capability will be added in future releases. |
tuning.chi |
tuning constant for the S-estimator.
The default, 1.54764, yields a 50% breakdown estimator. |
bb |
expected value under the normal model of the
"chi" function with tuning constant equal to
tuning.chi. This is used to compute the S-estimator. |
tuning.psi |
tuning constant for the re-descending M-estimator.
The choice 4.685061 yields an estimator with asymptotic
efficiency of 95% for normal errors. |
max.it |
integer specifying the maximum number of IRWLS iterations. |
groups |
This parameter is for the fast-S algorithm. Number of random subsets to use when the data set is large. |
n.group |
This parameter is for the fast-S algorithm. Size of
each of the groups above. |
k.fast.s |
This parameter is for the fast-S algorithm. Number of local improvement steps for each re-sampling candidate. |
compute.rd |
logical indicating if robust distances (based on
the MCD robust covariance estimator covMcd) are to be
computed for the robust diagnostic plots. This may take some
time to finish, particularly for large data sets. |
Matias Salibian-Barrera
lmrob, also for references and examples.